Many conventional methods have been proposed to generate ghost-free HDR images. They can be roughly divided into alignment before merging methods and motion rejection methods. The former approach aligns the LDR images before merging them. [1]}, [2]}, [3]} used optical flow to estimate the motions. These optical flow-based methods rely heavily on the accuracy of the estimation. Many studies use the PatchMatch algorithm [4]} to pre-align LDR images. Sen et al. [5]} proposed a patch-based energy minimization method and used a joint optimization strategy to integrate the alignment and HDR reconstruction processes. Hu et al. [6]} optimized the aligned LDR images based on intensity mapping function and gradient consistencies on the transformed domain. However, these methods are not very effective for large displacements and have a high computational cost. For the motion rejection methods, they first align the input LDR images at the overall level, and then reject unaligned pixels. Gallo et al. [7]} proposed to predict the color in various exposures and compare it with the original values to detect motions. Khan et al. [8]} proposed to assign fusion weights by calculating the probability of a given pixel belonging to the background. Heo et al. [9]} proposed to detect unaligned regions by calculating the joint probability density between LDR images. Lee et al. [10]} employed rank minimization to generate ghost-free images. A common drawback of these methods is that they ignore useful information about the abandoned pixels.
